no code implementations • NAACL (ACL) 2022 • Greta Tuckute, Aalok Sathe, Mingye Wang, Harley Yoder, Cory Shain, Evelina Fedorenko
The modular design of SentSpace allows researchersto easily integrate their own feature computation into the pipeline while benefiting from acommon framework for evaluation and visualization.
no code implementations • EMNLP (FEVER) 2021 • Aalok Sathe, Joonsuk Park
Automatic fact-checking is crucial for recognizing misinformation spreading on the internet.
no code implementations • 15 May 2024 • Anna A. Ivanova, Aalok Sathe, Benjamin Lipkin, Unnathi Kumar, Setayesh Radkani, Thomas H. Clark, Carina Kauf, Jennifer Hu, R. T. Pramod, Gabriel Grand, Vivian Paulun, Maria Ryskina, Ekin Akyurek, Ethan Wilcox, Nafisa Rashid, Leshem Chosen, Roger Levy, Evelina Fedorenko, Joshua Tenenbaum, Jacob Andreas
We present Elements of World Knowledge (EWOK), a framework for evaluating world modeling in language models by testing their ability to use knowledge of a concept to match a target text with a plausible/implausible context.
1 code implementation • EMNLP (MRL) 2021 • Karthikeyan K, Aalok Sathe, Somak Aditya, Monojit Choudhury
Multilingual language models achieve impressive zero-shot accuracies in many languages in complex tasks such as Natural Language Inference (NLI).
1 code implementation • ACL (SIGMORPHON) 2021 • Saujas Vaduguru, Aalok Sathe, Monojit Choudhury, Dipti Misra Sharma
Neural models excel at extracting statistical patterns from large amounts of data, but struggle to learn patterns or reason about language from only a few examples.
1 code implementation • CONLL 2020 • Pratik Joshi, Somak Aditya, Aalok Sathe, Monojit Choudhury
Pre-trained Transformer-based neural architectures have consistently achieved state-of-the-art performance in the Natural Language Inference (NLI) task.
no code implementations • LREC 2020 • Aalok Sathe, Salar Ather, Tuan Manh Le, Nathan Perry, Joonsuk Park
However, such datasets suffer from limited applicability due to the synthetic nature of claims and/or evidence written by annotators that differ from real claims and evidence on the internet.